code: def get_dinov2_config(model_name, image_classifier=False):
      config = Dinov2Config(image_size=518, patch_size=14)
  
      # size of the architecture
      if "vits" in model_name:
          config.hidden_size = 384
          config.num_attention_heads = 6
      elif "vitb" in model_name:
          pass
      elif "vitl" in model_name:
          config.hidden_size = 1024
          config.num_hidden_layers = 24
          config.num_attention_heads = 16
      elif "vitg" in model_name:
          config.use_swiglu_ffn = True
          config.hidden_size = 1536
          config.num_hidden_layers = 40
          config.num_attention_heads = 24
      else:
          raise ValueError("Model not supported")
  
      if image_classifier:
          repo_id = "huggingface/label-files"
          filename = "imagenet-1k-id2label.json"
          config.num_labels = 1000
          config.id2label = json.load(
              open(
                  hf_hub_download(
                      repo_id, filename, repo_type="dataset"
                  ),
                  "r",
              )
          )
          config.id2label = {
              int(k): v for k, v in config.id2label.items()
          }
  
      return config
  
file_path: transformers/src/transformers/models/dinov2/convert_dinov2_to_hf.py
imports: import argparse
  import json
  from pathlib import Path
  import requests
  import torch
  import torch.nn
  from huggingface_hub import hf_hub_download
  from PIL import Image
  from torchvision import transforms
  from transformers import BitImageProcessor, Dinov2Config, Dinov2ForImageClassification, Dinov2Model
  from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
  from transformers.utils import logging
label_desc: Use image node_id 1 for calling a global function with example usage: get_dinov2_config(model_name, image_classifier) and returns: config